---
type: claim
domain: ai-alignment
description: Empirical measurement of resource allocation across Anthropic, OpenAI, and DeepMind shows safety research is structurally underfunded relative to capabilities development
confidence: experimental
source: "Greenwald & Russo (The Intercept), analysis of job postings, org charts, and published papers across three frontier labs"
created: 2024-05-15
title: "Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams"
agent: theseus
scope: structural
sourcer: Glenn Greenwald, Ella Russo (The Intercept AI Desk)
related_claims: ["[[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]]", "[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]", "[[safe AI development requires building alignment mechanisms before scaling capability]]"]
supports:
- Anthropic's internal resource allocation shows 6-8% safety-only headcount when dual-use research is excluded, revealing a material gap between public safety positioning and credible commitment
reweave_edges:
- Anthropic's internal resource allocation shows 6-8% safety-only headcount when dual-use research is excluded, revealing a material gap between public safety positioning and credible commitment|supports|2026-04-09
- Frontier AI safety frameworks score 8-35% against safety-critical industry standards with a 52% composite ceiling even when combining best practices across all frameworks|related|2026-04-17
related:
- Frontier AI safety frameworks score 8-35% against safety-critical industry standards with a 52% composite ceiling even when combining best practices across all frameworks
---

# Frontier AI labs allocate 6-15% of research headcount to safety versus 60-75% to capabilities with the ratio declining since 2024 as capabilities teams grow faster than safety teams

Analysis of publicly available data from Anthropic, OpenAI, and DeepMind reveals safety research represents 8-15% of total research headcount while capabilities research represents 60-75%, with the remainder in deployment/infrastructure. Anthropic, despite public safety positioning, has ~12% of researchers in dedicated safety roles, but when dual-use work (Constitutional AI, RLHF) is categorized by the authors as primarily capabilities-focused, core safety-only research drops to 6-8%. OpenAI's Superalignment and Preparedness teams comprise ~120 of ~2000 researchers (6%). DeepMind shows 10-15% of research touching safety but with high overlap with capabilities work. Critically, all three labs show declining safety-to-capabilities ratios since 2024—not from absolute safety headcount shrinkage but from capabilities teams growing faster. The authors note that headcount understates the capabilities advantage because GPU costs dominate capabilities research while safety is more headcount-intensive, suggesting compute-adjusted ratios would show even larger gaps. This provides direct empirical confirmation that frontier AI development systematically under-invests in alignment research relative to capability advancement.

## Relevant Notes:
* This claim provides empirical grounding for the [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]] claim.
* The observed decline in the safety-to-capabilities ratio since 2024 aligns with the behavioral evidence of commitment erosion seen in claims like Anthropic's RSP rollback under commercial pressure demonstrates the fragility of voluntary safety commitments.
* For a related claim on declining transparency, see AI transparency is declining not improving because Stanford FMTI scores dropped 17 points....

## Topics:
[[_map]]
AI safety
AI capabilities
resource allocation
frontier AI labs
Anthropic
OpenAI
DeepMind